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restrictedSep 2019View details →

Mobile Custom Silicone Mask Attack Dataset (CSMAD-Mobile)

This dataset consists of face & silicon masks images from 8 different subjects captured with 3 different smartphones. This dataset consists of images captured from 8 different bona fide subjects using three different smartphones (iPhone X, Samsung S7 and Samsung S8). For each subject within the database, varying number of samples are collected using all the three phones. Similarly, the silicone masks of each of the subject is collected using three phones. The masks, each costing about USD 4000, have been manufactured by a professional special-effects company. For the bona fide presentations of the same eight subjects, each data subject is asked to pose in a manner compliant to standard portrait capture. The data is captured indoors, with adequate artificial lighting. Silicone mask presentations have been captured under similar conditions, by placing the masks on their bespoke support provided by the manufacturer, with prosthetic eyes and silicone eye sockets. The database is organized in three folders corresponding to three smartphones and further each subject within the database is organized in sub-folders. The files are named using the convention "PHONE/CLASS/SUBJECTNUMBER/PHONEIDENTIFIER-PRESENTATION-SUBJECTNUMBER-SAMPLENUMBER.jpg". <ul> <li>PHONE is iPhone, SamS7 or SamS8 corresponding to iPhone, Samsung S7 and Samsung S8 respectively.</li> <li>CLASS is "Bona" or "Mask" indicating the bona fide presentation or mask presentation respectively.</li> <li>SUBJECTNUMBER is "s1" to "s8" indicating 8 subjects in the database.</li> <li>PHONEIDENTIFIER is the two letter keyword as given by "ip", "s7" and "s8" corresponding to iPhone, Samsung S7 and Samsung S7 respectively.</li> <li>PRESENTATION identifies bona-fide or mask-attack presentation using 2 letter identifier "bp" or "ap".</li> <li>SAMPLENUMBER indicates the sample number of the subject.</li> </ul>   Reference If you publish results using this dataset, please cite the following publication. “Custom Silicone Face Masks - Vulnerability of Commercial Face Recognition Systems & Presentation Attack Detection”, R. Raghavendra, S. Venkatesh, K. B. Raja, S. Bhattacharjee, P. Wasnik, S. Marcel, and C. Busch. IAPR/IEEE International Workshop on Biometrics and Forensics (IWBF), 2019. <a href="https://doi.org/10.1109/IWBF.2019.8739236">10.1109/IWBF.2019.8739236</a> <a href="https://publications.idiap.ch/index.php/publications/show/4065">https://publications.idiap.ch/index.php/publications/show/4065</a>

restrictedNov 2018View details →

Custom Silicone Mask Attack Dataset (CSMAD)

The Custom Silicone Mask Attack Dataset (CSMAD) contains presentation attacks made of six custom-made silicone masks. Each mask cost about USD 4000. The dataset is designed for face presentation attack detection experiments. The Custom Silicone Mask Attack Dataset (CSMAD) has been collected at the Idiap Research Institute. It is intended for face presentation attack detection experiments, where the presentation attacks have been mounted using a custom-made silicone mask of the person (or identity) being attacked. The dataset contains videos of face-presentations, as a set of files specifying the experimental protocol corresponding the experiments presented in the corresponding publication.   Reference If you publish results using this dataset, please cite the following publication. Sushil Bhattacharjee, Amir Mohammadi and Sebastien Marcel: "Spoofing Deep Face Recognition With Custom Silicone Masks." in Proceedings of International Conference on Biometrics: Theory, Applications, and Systems (BTAS), 2018. <a href="https://doi.org/10.1109/BTAS.2018.8698550">10.1109/BTAS.2018.8698550</a> <a href="https://publications.idiap.ch/index.php/publications/show/3887">http://publications.idiap.ch/index.php/publications/show/3887</a>   Data Collection Face-biometric data has been collected from 14 subjects to create this dataset. Subjects participating in this data-collection have played three roles: targets, attackers, and bona-fide clients. The subjects represented in the dataset are referred to here with letter-codes: A .. N. The subjects A..F have also been targets. That is, face-data for these six subjects has been used to construct their corresponding flexible masks (made of silicone). These masks have been made by Nimba Creations Ltd., a special effects company. Bona fide presentations have been recorded for all subjects A..N. Attack presentations (presentations where the subject wears one of 6 masks) have been recorded for all six targets, made by different subjects. That is, each target has been attacked several times, each time by a different attacker wearing the mask in question. This is one way of increasing the variability in the dataset. Another way we have augmented the variability of the dataset is by capturing presentations under different illumination conditions. Presentations have been captured in four different lighting conditions: <ul> <li>flourescent ceiling light only</li> <li>halogen lamp illuminating from the left of the subject only</li> <li>halogen lamp illuminating from the right only</li> <li>both halogen lamps illuminating from both sides simultaneously</li> </ul> All presentations have been captured with a green uniform background. See the paper mentioned above for more details of the data-collection process.   Dataset Structure The dataset is organized in three subdirectories: ‘attack’, ‘bonafide’, ‘protocols’. The two directories: ‘attack’ and ‘bonafide’ contain presentation-videos and still images for attacks and bona fide presentations, respectively. The folder ‘protocols’ contains text files specifying the experimental protocol for vulnerability analysis of face-recognition (FR) systems. The number of data-files per category are as follows: <ul> <li>‘bonafide’: 87 videos, and 17 still images (in .JPG format). The still images are frontal face images captured using a Nikon Coolpix digital camera.</li> <li>‘attack’: 159, organized in two sub-folders – ‘WEAR’ (108 videos), and ‘STAND’ (51 videos)</li> </ul> The folder ‘attack/WEAR’ contains videos where the attack has been made by a person (attacker) wearing the mask of the target being attacked. The ‘attack/STAND’ folder contains videos where the attack has been made using a the target’s mask mounted on an appropriate stand.   Video File Format The video files for the face-presentations are in ‘hdf5’ format (with file-extensions ‘.h5’. The folder structure of the hdf5 file is shown in Figure 1. Each file contains data collected using two cameras: <ul> <li>RealSense SR300 (from Intel): collects images/videos in visible-light (RGB color) , near infrared (NIR) @ 860nm wavelength, and depth maps</li> <li>Compact Pro (from Seek Thermal): collects thermal (long-wave infrared (LWIR)) images.</li> </ul> As shown in Figure 1, frames from the different channels (color, infrared, depth, thermal) from he two cameras are stored in separate directory-hierarchies in the hdf5 file. Each file respresents a video of approximately 10 seconds, or roughly, 300 frames. In the hdf5 file, the directory for SR300 also contains a subdirectory named ‘aligned_color_to_depth’. This folder contains post-processed data, where the frames of depth channel have been aligned with those of the color channel based on the time-stamps of the frames.   Experimental Protocol The ‘protocols’ folder contains text files that specify the protocols for vulnerability analysis experiments reported in the paper mentioned above. Please see the README file in the protocols folder for details.

restrictedDec 2018View details →

UDP Flood Attack Pattern on Internet of Things Network Dataset

Investigating UDP Flood Attack Pattern on Internet of Things Network status: on review Abstract: UDP does not have mechanism for retransmission when a transmitting error happens, it makes this protocol to be used as a DDoS attack tool against Internet of Things (IoTs) networks. This research work attempts to analyze the UDP Flood attacks packets dataset captured from an Io|T testbed network by Wireshark. A feature extraction process on generated CSV file was performed and then the feature extraction result are examined to find patterns of UDP flood attack packet. Lastly, the patterns are visualized to provide easy pattern recognition.

restrictedDec 2018View details →

Ping Flood Attack Pattern Recognition on Internet of Things Network Dataset

Ping Flood Attack Pattern Recognition using K-Means Algorithm in Internet of Things (IoT) Network  status: on repository Abstract — This work investigates ping flood attack pattern recognition on Internet of Things (IoT) network. Experiments are conducted on WiFi communication with three different scenarios: normal traffic, attack traffic, and normal-attack combination traffic to create normal dataset, attack dataset, and normal attack (combined) dataset. The datasets are grouped into two clusters i.e.: (i) normal cluster and (ii) attack cluster. Clustering results using implemented K-Means algorithm show the average number of packets on the cluster of attack in total is 95,931 packets, and the average packets on normal cluster in total is 4,068 packets. Accuracy level of the clustering results then is calculated using confusion matrix equation. Based on the confusion matrix calculation, accuracy of clustering using implemented K-Means algorithm was 99.94%. The true negative rate reaches up to 98.62%, true positive rate is 100%, the false negative rate is 0%, and the false positive rate reaches 1.38%.

restrictedcc-by-4.0Jun 2024View details →

Shark attacks in New Caledonia from 1980 to 2022

This dataset encompasses detailed records of shark attack incidents in New Caledonia from 1980 to 2022. It is designed to support a multi-criteria analysis of these incidents, providing insights into the conditions and characteristics surrounding each event. Attributes :  1. N: Sequential number of the incident. 2. DATE: Date of the shark attack (YYYY-MM-DD format). 3. YEAR: Year of the incident. 4. MONTH: Month of the incident. 5. DAY: Day of the week when the incident occurred. 6. HOUR: Time of the incident (24-hour format). 7. HOURTYPO: Time range category (e.g., 13-15 for 1 PM to 3 PM). 8. SEASON: Season during which the attack occurred (Summer, Winter, etc.). 9. WEEKEND: Indicates whether the incident occurred on a weekend (Weekend or Week). 10. MORNING: Time of day (Morning, Afternoon, etc.). 11. ZONE: Specific zone or region within the site (e.g., Noumea, East, Loyalty). 12. WIND: Windward or leeward side. 13. RAINJ: Rainfall on the day of the incident (in millimeters). 14. RAINJ3: Cumulative rainfall over the past three days (in millimeters). 15. SWELL: Swell height (in meters). 16. CLOUD: Cloud cover percentage. 17. TURB: Water turbidity (e.g., Slightly turbid). 18. SCOREMOON: Lunar phase during the incident (e.g., Full or new moon). 19. GENDER: Gender of the victim (Male or Female). 20. AGE: Age of the victim. 21. ACTIVITY: Activity the victim was engaged in during the attack (e.g., Spearfisher, Swimmer). 22. GRAV: Severity of the injury (e.g., Significant bite, Minor bite). 23. INJURY: Outcome of the attack (e.g., Non-fatal). 24. SHARKTYPE: Type of shark involved (if identified). 25. SHARKCAT: Category of the shark (e.g., Great White, Tiger Shark). 26. SHARKHEIGHT: Estimated length of the shark (in meters). Usage Notes: This dataset is intended for researchers and analysts studying shark attack patterns, environmental influences on shark behavior, and risk factors associated with shark-human interactions. It provides comprehensive details necessary for performing statistical analyses and comparative studies between New Caledonia and Reunion Island. Data Source: The data has been compiled from various local and international databases, reports, and eyewitness accounts to ensure accuracy and completeness.

restrictedApr 2023View details →

code of ML based on DDoS attack

code of ML based on DDoS attack.  Note: the two files (dataset) are publically placed on kaggle website we have taken and used it for our research work, however we are sharing here to fulfill requirement of the journal. kaggle link of Dataset: https://www.kaggle.com/code/hamzasamiullah/ml-analysis-application-layer-dos-attack-dataset/notebook

restrictedJul 2023View details →

PHyMAtt (Personalised Hygienic Mask Attacks)

This dataset was used to perform the experiments reported in the IJCB2023 paper "Can personalised hygienic masks be used to attack face recognition systems?". The dataset consists of face videos captured using the ‘selfie’ cameras of five different smartphones : Apple iPhone 12, Apple iPhone 6s, Xiaomi Redmi 6 Pro, Xiaomi Redmi 9A and Samsung Galaxy S9. The dataset contains : <ul> <li>Bona-fide face videos: 1400 videos of bona-fide (real, non-attack) faces. In total, there are 70 identities (data subjects). Each video is 10 seconds long, where for the first 5 seconds the data subject was required to stay still and look at the camera, then for the last 5 seconds the subject was asked to turn their head from one side to the other (such that profile views could be captured). The videos were acquired indoors, under normal office lighting conditions. The data subjects were volunteers, who were required to be present during two recording sessions, which on average were separated by about three weeks. In each recording session, the volunteers were asked to record a video of their own face using the front (i.e., selfie) camera of each of the five smartphones mentioned earlier. The face data was additionally captured while the data subjects wore plain (not personalised) hygienic masks, to simulate the scenario where face recognition might need to be performed on a masked face (e.g., during a pandemic like COVID-19).</li> <li> Attacks: <ul> <li> Personalised hygienic mask attacks: Video recordings of an impostor wearing personalised hygienic masks (one at a time), on which the bottom part of each data subject’s face is printed. Please note that the dataset contains 350 personalised hygienic mask attack videos, whereas the IJCB2023 paper mentioned 345 videos. This is because, for the experiments reported in the paper, we excluded the videos of the attacker wearing their own hygienic mask (since the "attacker" was one of the 70 data subjects). </li> <li> Print attacks: 1400 video recordings of the data subjects’ face photos printed on A4 matte paper, which was held up to the smartphone’s camera. </li> <li> Replay attacks: 2800 video recordings of bona-fide face videos that were replayed to the target smartphone’s camera. Different phones were paired, such that one of the pair was used to replay the bona-fide videos while the second (attacked) phone recorded the videos using its front camera. </li> </ul> </li> </ul>   Reference If you use the data for your research or publication, please cite the following paper : <a href="https://publications.idiap.ch/authors/show/1883">Komaty, Alain</a>, <a href="https://publications.idiap.ch/authors/show/1934">Krivokuca Hahn, Vedrana</a>, <a href="https://publications.idiap.ch/authors/show/3114">Ecabert, Christophe</a> and <a href="https://publications.idiap.ch/authors/show/65">Marcel, Sébastien</a>, <a href="https://publications.idiap.ch/publications/show/5093">Can personalised hygienic masks be used to attack face recognition systems?</a>, in: Proceedings of IEEE International Joint Conference on Biometrics (IJCB2023), 2023

geo12/100

Complement Membrane Attack Complexes Disrupt Proteostasis to Function as Intracellular Alarmins

GEO Series GSE268767. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenJun 2024View details →

restrictedMay 2020View details →

eXtended Custom Silicone Mask Attack Dataset (XCSMAD)

Description The eXtended Custom Silicone Mask Attack Dataset (XCSMAD) consists of 535 short video recordings of both bona fide and presentation attacks (PA) from 72 subjects. The attacks have been created from custom silicone masks. Videos have been recorded in RGB (visual spectra), near infrared (NIR), and thermal (LWIR) channels. A complete preprocessed data for the aforementioned videos and bona fide images (as a part of experiments related to vulnerability assessment) have been provided to facilitate reproducing experiments from the reference publication, as well as to conduct new experiments. The details of preprocessing can be found in the reference publication. The implementation of all experiments described in the reference publication is available at <a href="https://gitlab.idiap.ch/bob/bob.paper.xcsmad_facepad">https://gitlab.idiap.ch/bob/bob.paper.xcsmad_facepad</a>   Experimental protocols The reference publication considers two experimental protocols: grandtest and cross-validation (cv). For a frame-level evaluation, 50 frames from each video have been used in both protocols. For the grandtest protocol, videos were divided into train, dev, and eval groups. Each group consists of unique subset of clients. (The videos corresponding to any specific subjects in one group are a part of single group). For cross-validation (cv) experiments, a 5-fold protocol has been devised. Videos from XCSMAD have been split into 5 folds with non-overlapping clients. Using these five partitions, 5 testprotocols (cv0, · · · , cv4) have been created such that in each protocol, four of the partitions are used for training, and the remaining one is used for evaluation.   Reference If you use this dataset, please cite the following publication: <pre>@article{Kotwal_TBIOM_2019, author = {Kotwal, Ketan and Bhattacharjee, Sushil and Marcel, S\'{e}bastien}, title = {<a href="https://publications.idiap.ch/index.php/publications/show/4145">Multispectral Deep Embeddings As a Countermeasure To Custom Silicone Mask Presentation Attacks</a>}, journal = {IEEE Transactions on Biometrics, Behavior, and Identity Science}, publisher = {{IEEE}}, year = {2019}, } </pre>

restrictedApr 2020View details →

RECOD Mobile Presentation-Attack Dataset (RECOD-MPAD)

============= Introduction The RECOD Mobile Presentation-Attack Dataset (RECOD-MPAD) is intended for the study of presentation attacks (PAs, also known as spoof attempts) to facial recognition systems in mobile devices. It consists of frames depicting genuine attempts of unlocking a smartphone, as well as two types of presentation attacks: using printouts of the user face; or using electronic displays showing the user's face. More details can be found in the accompanying paper: Detecting face presentation attacks in mobile devices with a patch-based CNN and a sensor-aware loss function Waldir R. Almeida, Fernanda A. Andaló, Rafael Padilha, Gabriel Bertocco, William Dias, Ricardo da S. Torres, Jacques Wainer, Anderson Rocha PLoS ONE, 2020 (https://doi.org/10.1371/journal.pone.0238058) This dataset was developed as part of a research project at the RECOD lab of the Institute of Computing, University of Campinas, Brazil. ========== Statistics Number of users: 45 Men: 30/45 Glasses: 14/45 Beard: 13/45 Age range: 18-50 ============================= Basic metadata and file names Name format: <device>_<session>_<user>_<label>_<frame> Label:  0: real, genuine access attempt 1: printout attack - recaptured indoors 2: printout attack - recaptured outdoors (more light) 3: screen attack - large display (CCE TV) 4: screen attack - medium-sized display (HP monitor) Device: 1: motog3 2: xt1572 Session: 1: outdoors natural direct light 2: outdoors natural diffuse light/shadow 3: indoors, top main light 4: indoors, side light (sunlight coming through window or door) 5: indoors, low-light / noisy Example: 1_5_20_00_0050 ====================== Additional information In each sequence, the volunteers followed the same instructions: - Hold the phone as if using it normally, but keep close-to-frontal viewing angles - Rotate slowly (to change lighting and background)

restrictedNov 2020View details →

High-Quality Wide Multi-Channel Attack (HQ-WMCA)

The High-Quality Wide Multi-Channel Attack database (HQ-WMCA) database consists of 2904 short multi-modal video recordings of both bona-fide and presentation attacks. There are 555 bonafide presentations from 51 participants and the remaining 2349 are presentation attacks. The data is recorded from several channels including color, depth, thermal, infrared (spectra), and short-wave infrared (spectra). Reference paper: <pre>@ARTICLE{Heusch_TBIOM_2020, author = {Heusch, Guillaume and George, Anjith and Geissb{\"u}hler, David and Mostaani, Zohreh and Marcel, S{\'{e}}bastien}, title = {Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks}, journal = {IEEE Transactions on Biometrics, Behavior, and Identity Science}, year = {2020}, publisher = {IEEE}, url = {http://publications.idiap.ch/downloads/papers/2020/Heusch_TBIOM_2020.pdf} }</pre>   Preprocessed images for some of the channels are also provided for the data used in the reference publication. The HQ-WMCA database is produced at Idiap within the framework of "IARPA BATL" project and it is intended for research, development, and testing in biometrics and biomedical analysis. More details about the data can be found in the following research report. <pre>@TECHREPORT{Mostaani_Idiap-RR-22-2020, author = {Mostaani, Zohreh and George, Anjith and Heusch, Guillaume and Geissenbuhler, David and Marcel, S{\'{e}}bastien}, projects = {Idiap, ODIN/BATL}, month = {9}, title = {The High-Quality Wide Multi-Channel Attack (HQ-WMCA) database}, type = {Idiap-RR}, number = {Idiap-RR-22-2020}, year = {2020}, institution = {Idiap}, pdf = {https://publidiap.idiap.ch/downloads//reports/2020/Mostaani_Idiap-RR-22-2020.pdf} }</pre>

restrictedcc-by-4.0Nov 2024View details →

Dataset of Thesis - Attacks on the Cloud: Unveiling Cyber Assaults on Cloud Infrastructure Through Honeypot Analysis

Open the record for dataset details and reuse information.

restrictedJan 2022View details →

in-Vehicle Face Presentation Attack Detection (VFPAD)

Description The in-Vehicle Face Presentation Attack Detection (VFPAD) dataset consists of 4046 bona-fide recordings from 40 subjects, and 1790 attack presentation videos from a total of 89 PAIs (presentation attack instruments). These presentations have been captured using an NIR camera (940 nm) placed on the steering wheel of the car, while NIR illuminators have been fixed on both front pillars (adjacent to the wind-shield) of the car. The bona-fide videos represent 24 male and 16 female subjects of various ethnicities. The PAI species used to construct this dataset include photo-prints, digital displays (for replay attacks), rigid 3D masks, and flexible 3D masks made of silicone.   Data Collection The videos comprising this dataset represent bona-fide and attack presentations under a range of variations: <ul> <li>Environmental variations: presentations have been recorded in four sessions, each under different environmental conditions (outdoor sunny; outdoor cloudy; indoor dimly-lit; and indoor brightly-lit)</li> <li>Different scenarios: bona-fide presentations for each subject have been captured with variety of appearances: with/without glasses, with/without hat, etc.</li> <li>Illumination variations: two illumination conditions have been used: ‘uniform’ (both NIR illuminators switched on), and ‘non-uniform’ (only the left NIR-illuminator switched on), and</li> <li>Pose variations: two poses (‘angles’) have been used: ‘front’: the subject looks ahead at the road; and ‘below’: subject looks straight into the camera.</li> </ul>   Citation If you use the dataset, please cite the following publication: @article{IEEE_TBIOM_2021,   author = {Kotwal, Ketan and Bhattacharjee, Sushil and Abbet, Philip and Mostaani, Zohreh and Wei, Huang and Wenkang, Xu and Yaxi, Zhao and Marcel, S\'{e}bastien},   title = {Domain-Specific Adaptation of CNN for Detecting Face Presentation Attacks in NIR},   journal = {IEEE Transactions on Biometrics, Behavior, and Identity Science},   publisher = {{IEEE}},   year={2022},   volume={4},   number={1},   pages={135--147},   doi={10.1109/TBIOM.2022.3143569} }

restrictedJan 2022View details →

Analysis of heart-rate variability during angioedema attacks in patients with hereditary c1-inhibitor deficiency

Database from the article Perego F, De Maria B, Bova M, Petraroli A, Marcelli Cesoni A, De Grazia V, Zingale LC, Porta A, Spadaro G, Dalla Vecchia LA. Analysis of Heart-Rate Variability during Angioedema Attacks in Patients with Hereditary C1-Inhibitor Deficiency. Int J Environ Res Public Health. 2021 Mar 12;18(6):2900. doi: 10.3390/ijerph18062900. PMID: 33809031; PMCID: PMC8002127. Abstract C1-inhibitor hereditary angioedema (C1-INH-HAE) is a rare disease characterized by self-limiting edema associated with localized vasodilation due to increased levels of circulating bradykinin. C1-INH-HAE directly influences patients' everyday lives, as attacks are unpredictable in frequency, severity, and the involved anatomical site. The autonomic nervous system could be involved in remission. The cardiac autonomic profile has not yet been evaluated during the attack or prodromal phases. In this study, a multiday continuous electrocardiogram was obtained in four C1-INH-HAE patients until attack occurrence. Power spectral heart rate variability (HRV) indices were computed over the 4 h preceding the attack and during the first 4 h of the attack in three patients. Increased vagal modulation of the sinus node was detected in the prodromal phase. This finding may reflect localized vasodilation mediated by the release of bradykinin. HRV analysis may furnish early markers of an impending angioedema attack, thereby helping to identify patients at higher risk of attack recurrence. In this perspective, it could assist in the timing, titration, and optimization of prophylactic therapy, and thus improve patients' quality of life.

restrictedFeb 2022View details →

Spinal nociceptive sensitization and plasma palmitoylethanolamide levels during experimentally induced migraine attacks

This dataset comprises the evaluation of plasma anandamide (AEA) and palmitoylethanolamide (PEA) levels and spinal sensitization in a validated human model of migraine based on systemic nitroglycerin (NTG) administration. Twenty-four subjects with episodic migraine (MIG) and 19 healthycontrols (HC) underwent blood sampling and investigation of nociceptive withdrawal reflex thresholds (RTh: single-stimulusthreshold; TST: temporal summation threshold) before and 30 (T30), 60 (T60), and 120 (T120) minutes after sublingual NTGadministration (0.9 mg). At baseline, the MIG and HC groups were comparable for plasma AEA (P50.822) and PEA (P50.182)levels, and for RTh (P50.142) and TST values (P50.150). Anandamide levels increased after NTG administration (P50.022) inboth groups, without differences between them (P50.779). By contrast, after NTG administration, PEA levels increased in the MIGgroup at T120 (P50.004), while remaining stable in the HC group. Nitroglycerin administration induced central sensitization in theMIG group, which was recorded as reductions in RTh (P50.046) at T30 and T120, and in TST (P50.001) at all time points. In theHC group, we observed increases in RTh (P50.001) and TST (P50.008), which suggest the occurrence of habituation. We foundno significant correlations between the ES and neurophysiological parameters. Our findings suggest a role for PEA in the ictal phaseof episodic migraine. The ES does not seem to be directly involved in the modulation of NTG-induced central sensitization, which suggests that the observed PEA increase and spinal sensitization are parallel, probably unrelated, phenomena.

restrictedJun 2022View details →

Oculo-vestibular signs in experimentally induced migraine attacks: an exploratory analysis

This database includes the raw data linked with the study “Oculo-vestibular signs in experimentally induced migraine attacks: an exploratory analysis”. In this study, we aim to study the occurrence of oculo-vestibular signs (OVSs) during experimentally induced migraine attacks in 24 episodic migraine patients and 19 healthy controls exposed to sublingual nitroglycerin (NTG 0.9 mg). We recorded the following parameters: evaluation of Head Impulse Test (HIT), Nystagmus with and without fixation, and Test of Skew with cover test (according to the HINTS examination, a validated and sensitive tool for detecting central vestibular dysfunction[8]); evaluation of smooth pursuit, vergence, saccadic ocular movements, head-shaking test (HST). Modification or appearance of nystagmus without visual fixation and in the position of Rose (supine with head hanging down) were also assessed. The examination was performed at baseline and before hospital discharge (T-180 post-NTG administration). In the subjects with a migraine-like attack occurring within the 180-minute observation period, the oculo-vestibular examination was repeated at migraine onset (T-MIG+). Sixteen migraine patients developed a migraine-like headache; in 13 of them, the onset occurred during the 180-minute observation period.  Three migraine patients (12.5%) developed new-onset OVSs during the induction test, at T-MIG+. The documented OVSs were still present at T-180 in all three patients. Two subjects (ID = 2 and ID = 17) had a down-beating nystagmus, which was not modified by visual fixation or position of Rose. The third subject (ID = 21) showed a positive HIT with overt saccades; in this subject a left beating nystagmus evoked by the HST was also observed. None of the patients with a negative induction test (MIG- group) developed OVSs throughout the study. Of note, none of the 4 subjects with OVSs reported oculo-vestibular symptoms during the observation period. Finally, we did not find any OVSs in HC group across all time-points

Hate speech and personal attack dataset in French social media

This dataset contains 29109 French tweet ids and corresponding annotations for Hate Speech label and 39109 French tweet ids and corresponding annotations for Personal attack label. The creation of this dataset was part of the project DACHS “A Data-driven Approach to Countering Hate Speech” funded by the Rights, Equality and Citizenship Programme of the European Union.

Hate speech and personal attack dataset in Spanish social media

This dataset contains 37688 Spanish tweet ids and corresponding annotations for Hate Speech label and 37688 Spanish tweet ids and corresponding annotations for Personal attack label. The creation of this dataset was part of the project DACHS “A Data-driven Approach to Countering Hate Speech” funded by the Rights, Equality and Citizenship Programme of the European Union.

Hate speech and personal attack dataset in German social media

This dataset contains 43735 German tweet ids and corresponding annotations for Hate Speech label and 43734 German tweet ids and corresponding annotations for Personal attack label. The creation of this dataset was part of the project DACHS “A Data-driven Approach to Countering Hate Speech” funded by the Rights, Equality and Citizenship Programme of the European Union.

Hate speech and personal attack dataset in English social media

This dataset contains 92022 English tweet ids and corresponding annotations for Hate Speech label and 90892 English tweet ids and corresponding annotations for Personal attack label. The creation of this dataset was part of the project DACHS “A Data-driven Approach to Countering Hate Speech” funded by the Rights, Equality and Citizenship Programme of the European Union.